


import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE						:List[Any]			=  logging.get_logger(__name__)


SCREAMING_SNAKE_CASE						:Optional[Any]			=  {
    '''vocab_file''': '''vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
    '''merges_file''': '''merges.txt''',
}

SCREAMING_SNAKE_CASE						:Tuple			=  {
    '''vocab_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'''
        ),
    },
    '''tokenizer_config_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'''
        ),
    },
    '''merges_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'''
        ),
    },
}

SCREAMING_SNAKE_CASE						:List[Any]			=  '''</w>'''
SCREAMING_SNAKE_CASE						:Optional[Any]			=  '''@@ '''



def       _lowerCAmelCase    (				lowerCAmelCase_				:List[Any]				)->Tuple:
   '''simple docstring'''






   snake_case_            =       set()
   snake_case_            =       word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)				)
      snake_case_            =       char
   return pairs


# Speech2Text2 has no max input length
SCREAMING_SNAKE_CASE						:List[str]			=  {'''facebook/s2t-wav2vec2-large-en-de''': 10_24}



class      __lowerCAmelCase  (			__snake_case       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							VOCAB_FILES_NAMES
  _SCREAMING_SNAKE_CASE		     =							PRETRAINED_VOCAB_FILES_MAP
  _SCREAMING_SNAKE_CASE		     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _SCREAMING_SNAKE_CASE		     =							['input_ids', 'attention_mask']



  def __init__(  self							:							Tuple     ,					_lowerCAmelCase							:							List[str]     ,					_lowerCAmelCase							:							Dict="<s>"     ,					_lowerCAmelCase							:							int="<pad>"     ,					_lowerCAmelCase							:							str="</s>"     ,					_lowerCAmelCase							:							Optional[int]="<unk>"     ,					_lowerCAmelCase							:							List[Any]=False     ,					_lowerCAmelCase							:							str=None     ,					**_lowerCAmelCase							:							int     ,					) ->			Union[str, Any]:




     """simple docstring"""
     super().__init__(
         unk_token=_lowerCAmelCase     ,					bos_token=_lowerCAmelCase     ,					eos_token=_lowerCAmelCase     ,					pad_token=_lowerCAmelCase     ,					do_lower_case=_lowerCAmelCase     ,					**_lowerCAmelCase     ,					)

     snake_case_            =       do_lower_case

     with open(_lowerCAmelCase     ,					encoding="utf-8"	) as vocab_handle:
        snake_case_            =       json.load(_lowerCAmelCase	)
     snake_case_            =       {v: k for k, v in self.encoder.items()}

     if merges_file is None:
        logger.info(F'''No merges files provided. {self.__class__.__name__} can only be used for decoding.'''	)

        snake_case_            =       None
        snake_case_            =       None
     else:
        with open(_lowerCAmelCase     ,					encoding="utf-8"	) as merges_handle:
           snake_case_            =       merges_handle.read().split("\n"	)[:-1]

        snake_case_            =       [tuple(merge.split()[:2]	) for merge in merges]
        snake_case_            =       dict(zip(_lowerCAmelCase     ,					range(len(_lowerCAmelCase	)	)	)	)
        snake_case_            =       {}



  @property
  def        lowerCAmelCase__	(  self							:							Dict	) ->			str:




     """simple docstring"""
     return len(self.decoder	)



  def        lowerCAmelCase__	(  self							:							int	) ->			Any:




     """simple docstring"""
     return dict(self.encoder     ,					**self.added_tokens_encoder	)



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							List[str]	) ->			Tuple:




     """simple docstring"""
     snake_case_            =       tuple(token[:-1]	) + (token[-1] + BPE_TOKEN_MERGES,)
     if token in self.cache:
        return self.cache[token]
     snake_case_            =       get_pairs(_lowerCAmelCase	)

     if not pairs:
        return token

     while True:
        snake_case_            =       min(_lowerCAmelCase     ,					key=lambda _lowerCAmelCase	: self.bpe_ranks.get(_lowerCAmelCase     ,					float("inf"	)	)	)
        if bigram not in self.bpe_ranks:
           break
        snake_case_     ,     snake_case_            =       bigram
        snake_case_            =       []
        snake_case_            =       0
        while i < len(_lowerCAmelCase	):
           try:
              snake_case_            =       word.index(_lowerCAmelCase     ,					_lowerCAmelCase	)
           except ValueError:
              new_word.extend(word[i:]	)
              break
           else:
              new_word.extend(word[i:j]	)
              snake_case_            =       j

           if word[i] == first and i < len(_lowerCAmelCase	) - 1 and word[i + 1] == second:
              new_word.append(first + second	)
              i += 2
           else:
              new_word.append(word[i]	)
              i += 1
        snake_case_            =       tuple(_lowerCAmelCase	)
        snake_case_            =       new_word
        if len(_lowerCAmelCase	) == 1:
           break
        else:
           snake_case_            =       get_pairs(_lowerCAmelCase	)
     snake_case_            =       " ".join(_lowerCAmelCase	)
     if word == "\n  " + BPE_TOKEN_MERGES:
        snake_case_            =       "\n" + BPE_TOKEN_MERGES

     if word.endswith(_lowerCAmelCase	):
        snake_case_            =       word.replace(_lowerCAmelCase     ,					""	)

     snake_case_            =       word.replace(" "     ,					_lowerCAmelCase	)
     snake_case_            =       word
     return word



  def        lowerCAmelCase__	(  self							:							List[Any]     ,					_lowerCAmelCase							:							Tuple	) ->			Any:




     """simple docstring"""






     if self.bpe_ranks is None:
        raise ValueError(
            "This tokenizer was instantiated without a `merges.txt` file, so"
            " that it can only be used for decoding, not for encoding."
            "Make sure to provide `merges.txt` file at instantiation to enable "
            "encoding."	)

     if self.do_lower_case:
        snake_case_            =       text.lower()

     snake_case_            =       text.split()

     snake_case_            =       []
     for token in text:
        if token:
           split_tokens.extend(list(self.bpe(_lowerCAmelCase	).split(" "	)	)	)

     return split_tokens



  def        lowerCAmelCase__	(  self							:							str     ,					_lowerCAmelCase							:							Any	) ->			Any:




     """simple docstring"""
     return self.encoder.get(_lowerCAmelCase     ,					self.encoder.get(self.unk_token	)	)



  def        lowerCAmelCase__	(  self							:							Any     ,					_lowerCAmelCase							:							Tuple	) ->			Dict:




     """simple docstring"""
     snake_case_            =       self.decoder.get(_lowerCAmelCase     ,					self.unk_token	)
     return result



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							int	) ->			Tuple:




     """simple docstring"""
     snake_case_            =       " ".join(_lowerCAmelCase	)

     # make sure @@ tokens are concatenated
     snake_case_            =       "".join(string.split(_lowerCAmelCase	)	)

     return string



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							str = None	) ->			Optional[Any]:




     """simple docstring"""






     if not os.path.isdir(_lowerCAmelCase	):
        logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
        return
     snake_case_            =       os.path.join(
         _lowerCAmelCase     ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
     snake_case_            =       os.path.join(
         _lowerCAmelCase     ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]	)

     with open(_lowerCAmelCase     ,					"w"     ,					encoding="utf-8"	) as f:
        f.write(json.dumps(self.encoder     ,					indent=2     ,					sort_keys=_lowerCAmelCase     ,					ensure_ascii=_lowerCAmelCase	) + "\n"	)

     snake_case_            =       0
     if self.bpe_ranks is None:
        return (vocab_file,)

     with open(_lowerCAmelCase     ,					"w"     ,					encoding="utf-8"	) as writer:
        for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,					key=lambda _lowerCAmelCase	: kv[1]	):
           if index != token_index:
              logger.warning(
                  F'''Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'''
                  " Please check that the tokenizer is not corrupted!"	)
              snake_case_            =       token_index
           writer.write(" ".join(_lowerCAmelCase	) + "\n"	)
           index += 1

     return (vocab_file, merges_file)








"""simple docstring"""



import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.12.2'):
		raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
		raise Exception('requires fairseq < v2')

logging.set_verbosity_info()
lowercase_							=							logging.get_logger(__name__)

lowercase_							=							'Hello, World!'
lowercase_							=							'en_XX'
def      UpperCAmelCase     (       _lowercase :   str			,   _lowercase :   str			,   _lowercase :   bool	)						-> Optional[int]:


	"""simple docstring"""




	lowerCAmelCase_      							=			Path('''data_bin'''	)
	lowerCAmelCase_      							=			FairseqXmodModel.from_pretrained(
	    model_name_or_path=str(Path(_lowercase	).parent	)			,   checkpoint_file=Path(_lowercase	).name			,   _name='''xmod_base'''			,   arch='''xmod_base'''			,   task='''multilingual_masked_lm'''			,   data_name_or_path=str(_lowercase	)			,   bpe='''sentencepiece'''			,   sentencepiece_model=str(Path(_lowercase	).parent / '''sentencepiece.bpe.model'''	)			,   src_dict=str(data_dir / '''dict.txt'''	)			,   )
	xmod.eval()  # disable dropout
	print(_lowercase	)

	lowerCAmelCase_      							=			xmod.model.encoder.sentence_encoder
	lowerCAmelCase_      							=			XmodConfig(
	    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings			,   hidden_size=xmod.cfg.model.encoder_embed_dim			,   num_hidden_layers=xmod.cfg.model.encoder_layers			,   num_attention_heads=xmod.cfg.model.encoder_attention_heads			,   intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim			,   max_position_embeddings=5_1_4			,   type_vocab_size=1			,   layer_norm_eps=1E-5			,   pre_norm=xmod.cfg.model.encoder_normalize_before			,   adapter_reduction_factor=getattr(xmod.cfg.model			,   '''bottleneck'''			,   2	)			,   adapter_layer_norm=xmod.cfg.model.adapter_layer_norm			,   adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm			,   ln_before_adapter=xmod.cfg.model.ln_before_adapter			,   languages=xmod.cfg.model.languages			,   )
	if classification_head:
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

	print('''Our X-MOD config:'''			,   _lowercase	)

	lowerCAmelCase_      							=			XmodForSequenceClassification(_lowercase	) if classification_head else XmodForMaskedLM(_lowercase	)
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	lowerCAmelCase_      							=			xmod_sent_encoder.embed_tokens.weight
	lowerCAmelCase_      							=			xmod_sent_encoder.embed_positions.weight
	lowerCAmelCase_      							=			torch.zeros_like(
	    model.roberta.embeddings.token_type_embeddings.weight	)  # just zero them out b/c xmod doesn't use them.

	lowerCAmelCase_      							=			xmod_sent_encoder.layernorm_embedding.weight
	lowerCAmelCase_      							=			xmod_sent_encoder.layernorm_embedding.bias

	for i in range(config.num_hidden_layers	):
		# Encoder: start of layer
		lowerCAmelCase_      							=			model.roberta.encoder.layer[i]
		lowerCAmelCase_      							=			xmod_sent_encoder.layers[i]

		# self attention
		lowerCAmelCase_      							=			layer.attention.self
		if not (
		    xmod_layer.self_attn.k_proj.weight.data.shape
		    == xmod_layer.self_attn.q_proj.weight.data.shape
		    == xmod_layer.self_attn.v_proj.weight.data.shape
		    == torch.Size((config.hidden_size, config.hidden_size)	)
		):
			raise AssertionError('''Dimensions of self-attention weights do not match.'''	)

		lowerCAmelCase_      							=			xmod_layer.self_attn.q_proj.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn.q_proj.bias
		lowerCAmelCase_      							=			xmod_layer.self_attn.k_proj.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn.k_proj.bias
		lowerCAmelCase_      							=			xmod_layer.self_attn.v_proj.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn.v_proj.bias

		# self-attention output
		lowerCAmelCase_      							=			layer.attention.output
		if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
			raise AssertionError('''Dimensions of self-attention output weights do not match.'''	)
		lowerCAmelCase_      							=			xmod_layer.self_attn.out_proj.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn.out_proj.bias
		lowerCAmelCase_      							=			xmod_layer.self_attn_layer_norm.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn_layer_norm.bias

		# intermediate
		lowerCAmelCase_      							=			layer.intermediate
		if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
			raise AssertionError('''Dimensions of intermediate weights do not match.'''	)
		lowerCAmelCase_      							=			xmod_layer.fca.weight
		lowerCAmelCase_      							=			xmod_layer.fca.bias

		# output
		lowerCAmelCase_      							=			layer.output
		if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
			raise AssertionError('''Dimensions of feed-forward weights do not match.'''	)
		lowerCAmelCase_      							=			xmod_layer.fca.weight
		lowerCAmelCase_      							=			xmod_layer.fca.bias
		lowerCAmelCase_      							=			xmod_layer.final_layer_norm.weight
		lowerCAmelCase_      							=			xmod_layer.final_layer_norm.bias
		if bert_output.adapter_layer_norm is not None:
			lowerCAmelCase_      							=			xmod_layer.adapter_layer_norm.weight
			lowerCAmelCase_      							=			xmod_layer.adapter_layer_norm.bias

		if sorted(bert_output.adapter_modules.keys()	) != sorted(xmod_layer.adapter_modules.keys()	):
			raise AssertionError('''Lists of language adapters do not match.'''	)
		for lang_code, adapter in xmod_layer.adapter_modules.items():
			lowerCAmelCase_      							=			bert_output.adapter_modules[lang_code]
			lowerCAmelCase_      							=			xmod_layer.adapter_modules[lang_code]
			lowerCAmelCase_      							=			from_adapter.fca.weight
			lowerCAmelCase_      							=			from_adapter.fca.bias
			lowerCAmelCase_      							=			from_adapter.fca.weight
			lowerCAmelCase_      							=			from_adapter.fca.bias

		# end of layer

	if xmod_sent_encoder.layer_norm is not None:
		lowerCAmelCase_      							=			xmod_sent_encoder.layer_norm.weight
		lowerCAmelCase_      							=			xmod_sent_encoder.layer_norm.bias

	if classification_head:
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].dense.weight
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].dense.bias
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].out_proj.weight
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].out_proj.bias
	else:
		# LM Head
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.dense.weight
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.dense.bias
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.layer_norm.weight
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.layer_norm.bias
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.weight
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.bias

	# Let's check that we get the same results.
	lowerCAmelCase_      							=			xmod.encode(_lowercase	).unsqueeze(0	)  # batch of size 1
	model.roberta.set_default_language(_lowercase	)

	lowerCAmelCase_      							=			model(_lowercase	)[0]
	if classification_head:
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''](xmod.extract_features(_lowercase	)	)
	else:
		lowerCAmelCase_      							=			xmod.model(_lowercase			,   lang_id=[SAMPLE_LANGUAGE]	)[0]
	print(our_output.shape			,   their_output.shape	)
	lowerCAmelCase_      							=			torch.max(torch.abs(our_output - their_output	)	).item()
	print(F"""max_absolute_diff = {max_absolute_diff}"""	)  # ~ 1e-7
	lowerCAmelCase_      							=			torch.allclose(_lowercase			,   _lowercase			,   atol=1E-3	)
	print('''Do both models output the same tensors?'''			,   '''🔥''' if success else '''💩'''	)
	if not success:
		raise Exception('''Something went wRoNg'''	)

	Path(_lowercase	).mkdir(parents=_lowercase			,   exist_ok=_lowercase	)
	print(F"""Saving model to {pytorch_dump_folder_path}"""	)
	model.save_pretrained(_lowercase	)


if __name__ == "__main__":
		lowercase_							=							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--xmod_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		parser.add_argument(
		    '--classification_head', action='store_true', help='Whether to convert a final classification head.'
		)
		lowercase_							=							parser.parse_args()
		convert_xmod_checkpoint_to_pytorch(
		    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)